In 1895, Röntgen (Figure 1) was at the University of Wurzburg, Germany, experimenting with gaseous discharge tubes (Figure 2). When a high voltage is applied to the electrodes of a partially evacuated glass tube, a glow is observed. Back in 1838, Michael Faraday described the bright regions near the electrodes with a dark region in between (Faraday dark space). Later, physicist Johann Hittorf (1824–1914) observed glowing rays extending from the negative electrode to produce a fluorescent glow where they struck the glass walls. These were named "cathode rays" by Eugene Goldstein (1850–1931). Sir William Crookes (1832–1919) demonstrated that cathode rays could cast shadows of objects, turn a small wheel in their path, and be deflected by a magnet. In 1892, physicist Heinrich Hertz (1857–1894) showed that these rays could penetrate very thin metal foil. Röntgen, however, was to discover a different kind of ray. 

Röntgen was born on March 27, 1845, in Lennep, Prussia. His early schooling was in Holland. He received a diploma in mechanical engineering in 1868 from Zürich Polytechnic in Switzerland. One year later, he received his Ph.D. in the same subject for a thesis on the states of gases. He became an assistant to August Kundt, a professor of physics at Zürich, who awakened his interest in pure science. After a series of interim positions, he was appointed professor of physics at the University of Würzberg in 1889. 

On the evening of November 8, 1895, Röntgen enclosed his discharge tube in a thick, black carton, excluding all light. Working in a dark room, he observed a plate coated with barium platinocyanide phosphor become fluorescent when placed in the path of the rays. The fluorescence persisted as far as 2 m from the discharge tube. In an attempt to record these rays, he switched to photographic plates. He found that different objects placed in the path of the rays showed varying transparency when recorded on a photographic plate. He demonstrated the effect of thickness on the transparency by building a step-wise wedge of tinfoil and photographing it in the path of the new rays. 

Röntgen continued to experiment throughout the night. In fact, it is said that he ate and slept in his laboratory for a time. At one point, he held his wife's hand in the path of the rays for a short time. When the photographic plate was developed, it showed an image of her hand. The bones and a ring she was wearing cast a distinct shadow. The flesh, being more permeable to the rays, cast a fainter shadow. 

Röntgen showed that the new rays were produced by the impact of the cathode rays on material objects. He called them "X" rays because of their unknown nature. His first paper on the experiments, "On a New Kind of Ray, A Preliminary Communication," was submitted to the Wurzburg Physico-Medical Society on December 28, 1895, and was published before the end of the year (1). Very soon, many others were studying the new rays. 

Later, asked what his thoughts were at the moment of discovering the new rays, he replied, "I didn't think, I experimented." Indeed, his experimentation was so thorough that one investigator (Silvanus Thompson) wryly observed that Röntgen had left "little for others to do beyond elaborating his work"(2). 

It remained for physicist Max von Laue (1879–1960) and coworkers to show that X-rays have the same electromagnetic character as light, differing only in the higher frequency of vibration. (Von Laue won the Nobel Prize in physics in 1914 for discovery of the diffraction of X-rays by crystals.)

The applications of X-rays are ubiquitous today. Everyone has had a medical or dental X-ray. Industrial radiography is used for determination of defects in castings and weldments. X-ray fluorescence spectrometry is a key instrumental method for the determination of material chemical composition. X-ray diffraction is the most powerful tool for material structure determinations.

Sadly, the man who made all of this possible died penniless in 1923. He believed his discovery should be free to all and refused monetary gain. 

, a physicist by training, has some 30 years of experience in elemental spectrochemical analysis (OES and XRF). He is currently a consultant in this area from his home in Atibaia, São Paulo, Brazil. His other interests include mineralogy and history of science. Occasionally, he still plays the blues harmonica. He can be reached at 

(1) W.C. Roentgen, "On a New Kind of Rays," 

, 137 (1895). English translation online at 

(2) The Health Physics Society, "Figures in Radiation History" http://

www.orcbs.msu.Edu/radiation/radhistory/wilhelmrontgen.Html

(3) Roentgen Kuraturium Wurzberg. On the web at 

Alexi Assmus, "The Early History of X Rays," 

(2), 10–24 (Summer 1995). Available on the web at http://

www.slacStanford.Edu/pubs/beamline/25/2/25-2-assmus.Pdf

Wilhelm Conrad Röntgen and the Early History of the Roentgen Rays 

(Norman Publishing, Novato, California, 1993).

Articles

Atomic Perspectives

Volker Thomsen takes a look at the impact that the discovery of X-rays by Wilhelm Röntgen in 1895 has had on the world.

Wilhelm Conrad Röntgen and the Discovery of X-Rays

Biological warfare can be classified broadly in one of three ways: the deliberate poisoning of food or water with infectious material; the use of microorganisms, toxins (that is, biologically derived poisons), or animals (living or dead) in weapon systems; or implementation of biologically inoculated materials (for example, fabrics or tools) (1). Throughout recorded history, biological warfare has been manifested in each of these routes. For example, as early as the sixth century B.C., Assyrian warriors poisoned an adversary's water supply with rye ergot (

), a grain fungus that causes delusion, convulsion, and gangrene in humans and livestock (2). Since this initial demonstration of biology as a weapon, there have been numerous examples of intermittent application of virulent organisms or etiological by-products to reduce the military strength of a foe.

More recently, an increase in the number and severity of worldwide bioterrorist acts combined with information suggesting that several different biological agents of biosecurity concern have been weaponized by various groups around the globe highlights the fact that biological attacks are a plausible if not imminent reality (3,4). Notably, etiological agents such as bacterial endospores and viruses have become of particular interest recently. For example, the rapid and accurate discrimination of smallpox (for example,

 virion, from other exanthem-causing viruses has become a central concern in the contradiction of possible bioterrorist and agro-terrorist attacks.  

The current unavailability of reliable identification protocols underscores the urgency to develop methods for rapid identification of potential biological threat agents including 

 as early as possible (in-the-field). While latent threats of biological threat agents have been recognized for several decades, only recently has the urgency to address these threats through development of potentially field-deployable methods been acted upon (5). One sensing methodology that holds significant promise to fulfill this technological void is surface-enhanced Raman spectroscopy (SERS) (6).

Of late, SERS has experienced a resurgence in interest due to the high sensitivities attainable by most surface-enhanced spectroscopic techniques. A plausible explanation for a significant portion of the spectral amplification achieved through SERS can be attributed to an increase in the electromagnetic field strength encountered by the analyte. Briefly, this intensified electromagnetic field is generated when a metal surface (typically, Au, Ag, Pt, Pd, or Cu) is irradiated with the requisite wavelength of light, and metal conduction band electrons are excited to collective oscillation through a surface plasmon resonance. Additionally, with certain metal–analyte systems, enhancements of 4–14 orders of magnitude are achieved readily. Since early reports, its acceptance has not been as widespread as expected. This limited acceptance has in large part been due to the lack of fabrication reproducibility in most SERS substrate architectures. Relatively recent advances in SERS-active substrate fabrication have addressed this reproducibility issue through development of commercial-off-the-shelf (COTS), durable, biocompatible, and long shelf-life substrates (7). 

: All SERS measurements were acquired using a Renishaw inVia Reflex Raman microscope (Hoffman Estates, Illinois) equipped with a near-infrared (NIR) diode laser excitation source. Light from the high-power diode (λ = 785 nm) was focused onto the sample at the microscope stage through a 20× objective. The diode laser beam was circularized, by inserting a pinhole into the optical beam path with reduction of the maximum available laser power to ~20 mW. Before all measurements, the instrument was wavelength calibrated using an internal Si standard. Each SERS spectrum was collected from 350 cm

  in the instrument's Extended SynchroScan mode (10.00-s exposure time) with a spectral resolution better than 2 cm

  and is the result of a single accumulation unless otherwise stated. All spectra were saved in ASCII format for subsequent analysis using other software packages. 

: Au-coated Klarite SERS-active substrates were purchased from D3 Technologies LTD (Southampton, Hampshire, United Kingdom). To facilitate easy handling and positioning, the 6 mm × 10 mm chip (consisting of a 4 mm × 4 mm patterned region and an unpatterned Au reference area) is adhered to a standard microscope slide (25 mm × 75 mm) at the foundry. The substrates were used as received without further modification.  

: Bovine papular stomatitis virus (ATCC VR-801) and pseudocowpox virus (ATCC VR-634) intracellular mature virions (IMV) were purchased from American Type Culture Collection (Manassas, Virginia). The virions were prepared by propagation on bovine embryonic kidney cells for 4 days and 3 days, respectively. Subsequent to growth, mature virions of each species were purified by the CsCl gradient centrifugation method. Viral titers of 1.0 × 10

  virions/mL for each virus were prepared by serial dilution of each viral stock suspension in de-ionized water. Before SERS spectra acquisition, 1.00 μL of each viral suspension was deposited onto the substrate and the sample was dried at room temperature, leaving intact viral particles on the substrate surface.  

: The Unscrambler (version 7.6) chemometric development software was purchased from Applied Chemometrics (Sharon, Massachusetts) and was used to develop partial least squares regression algorithms for classification of unknown samples. Training spectra and data for classification were imported into this software as ASCII format. 

Through maturation of micromachining techniques and high-resolution analysis tools, it is currently possible to routinely fabricate devices with precisely placed building blocks or components (8,9). These efficient fabrication techniques and analysis tools have been leveraged to develop reproducibly manufacturable SERS-active substrates. Conceivably, variances associated with many proposed SERS-based detection approaches can be mitigated greatly by employing spatially uniform and reproducible SERS-active substrates. To address this technological challenge, recently, lithographically patterned surfaces promoted as SERS-active substrates have been made available in the marketplace. Illustrated in Figure 1 is a schematic representation of these SERS-active chips fabricated by anisotropically wet etching (1 0 0) oriented Si wafers followed by vapor deposition of approximately a 100-nm thick layer of Au. In short, these commercialized SERS chips contain a 4 mm × 4 mm patterned region surrounded by an uncorrugated reference region. Scanning electron microscopy (SEM) analysis of these patterned regions shows that the patterned surface is composed of C

-symmetric depressions (spaced at a period of 2 μm) and spatially uniform over areas occupying at least 0.1 mm

In addition to SEM, we have employed a well-established methodology proposed by the Rowlen group (10) to quantitatively assess the performance of these plasmonic surfaces for SERS applications. Specifically, 

-1,2-bis(4-pyridyl)ethylene (BPE) was used as a Raman-active target molecule to assess the substrate spatial uniformity and substrate-to-substrate reproducibility. Briefly, dilute ethanolic solutions of BPE were spin-coated onto the substrate surface (at approximately 1000 rpm) using a home-built spin-coater. As part of this analysis, SERS spectra were acquired at random sites across the substrate surface; this experiment was repeated over several COTS SERS-active substrates. Results of this analysis are depicted in Figure 2. As illustrated in Figure 2a, the mean SERS spectra of various densities of BPE are similar in shape and linear (with respect to analyte density) over more than one order of magnitude. The depicted results confirm that these surfaces are sensitive and spatially uniform, and have high substrate-to-substrate reproducibility. 

The high sensitivity, spatial uniformity, and substrate-to-substrate reproducibility of these COTS SERS-active substrates prompted us to further investigate utilizing these surfaces as a basis for the rapid detection/discrimination of potentially high-consequence biological threat agents such as smallpox (for example, 

's environmental robustness, to date it has been difficult to develop reliable techniques for its unambiguous identification in field-deployable applications. Further, clinical presentation of smallpox poses a challenge in medical diagnosis because it is nearly indistinguishable from several other external exanthems (11,12). For example, most physicians lack the experience to distinguish smallpox, human monkeypox, and chickenpox quickly. Rapid and accurate differential diagnosis of such conditions is especially critical because confirmation of a single case of smallpox represents a national security concern. Conversely, diagnosis of human monkeypox in disease naïve regions of the world (absent of additional indicators of biowarfare activity) or diagnosis of isolated cases of chickenpox do not represent a cause for alarm (13,14).  

Plotted in Figure 3 are representative, intensity-normalized SERS spectra of pseudocowpox virus (PCPV) and bovine papular stomatitis virus (BPSV) over the Raman shift range from 350 cm

 . Study of these virions is especially interesting because they are genetically similar to 

 (smallpox). In general, these viral spectra are dominated by features centered near 455-, 853-, 1003-, and 1168 cm

 . Comparison of BPSV and PCPV shows strong similarity between their spectral signatures. However, the BPSV spectrum contains spectral features at 539-, 853-, and 1168 cm

  that are absent in the PCPV spectrum. Further, these data imply that it is possible to distinguish BPSV and PCPV based upon their molecular composition and hence, SERS spectral signatures. 

Unique spectral features apparent in each spectrum make it possible to distinguish between these viruses by univariate (that is, dependent upon a single Raman shift frequency) calibration approaches. However, using most univariate calibration approaches, it is not possible to identify multiple bioanalytes simultaneously (that is, virions). An alternative approach to identification is to leverage the strengths of multivariate (that is, full spectrum) calibration; namely, ease of simultaneous identification of multiple analytes; highly correlated adjacent Raman shift frequencies improve classification precision as well as fault (that is, unknown interferents) detection; and developed classification algorithms can be automated to aid in identification of unknown viral samples by nontechnical personnel (15–18).  

One such multivariate calibration approach is the partial least squares 2 (PLS2) method. We have used this well-established chemometric approach to develop a personal computer-borne model that can be used for the rapid and accurate identification of virus samples. In concept, this model simultaneously utilizes two cooperative algorithms that are devoted to identification of samples that contain either BPSV or PCPV. Tandem application of these statistical algorithms facilitates the rapid classification (that is, identification) of unknown SERS spectral signatures. Briefly, these algorithms were trained using PCPV and BPSV samples over the viral titer range from 1 × 10

  virions/mL. Internal cross-validation analysis of this predictive model illustrates that it performs optimally using only eight principal components (PCs). 

To further validate performance of this model, 50 (25 PCPV and 25 BPSV) unknown viral SERS spectral signatures were classified. As listed in Table I, viral test samples used to validate performance of the developed model contain either BPSV or PCPV with viral titers ranging from 1 × 10

Table I: Viral test samples used to validate performance of developed predictive PLS2 algorithm 

Figures 4a and 4b show results of the simultaneous classification of the viral test samples based on the developed model. Shown in these figures are the viral test sample designation (that is, BPSV-S1 or PCPV-S1) and the probability of classification calculated by the PLS2 model. Integral to classification, we establish an arbitrary threshold at the 50% probability of classification level. In principle, probability of classification values above this threshold are indicative of viral species identification, while values below this threshold are insufficient for identification. Applying this filter, the predictive model correctly classifies all unknown viral samples with no false positives or false negatives. These data demonstrate that multivariate models trained using intrinsic viral SERS spectral signatures can be utilized to aid in efficient identification of intact 

 virions using these lithographically patterned SERS-active substrates. 

SEM and a well-established, quantitative SERS substrate assessment methodology have been used to characterize COTS SERS-active substrate architecture. Results of these analyses show that these substrates are sensitive, spatially uniform, and reproducible from substrate to substrate. High spatial uniformity and substrate-to-substrate reproducibility coupled with availability from a commercial source makes these substrates a prime candidate to be used as the foundation in development of novel surface-enhanced spectroscopic approaches.  

We have used these plasmonic surfaces to develop a novel SERS-based methodology for the rapid identification of intact 

 virions. Intrinsic SERS spectral signatures of BPSV and PCPV, taxonomically close relatives of smallpox (for example, 

), acquired with these substrates have been used as a basis set to develop a computer-borne PLS2 predictive model. This model is designed to quickly identify unknown viral samples that contain either BPSV or PCPV. To validate this model, we have categorized 50 unknown viral samples. Results of these validations illustrate that this model correctly classifies all of the viral samples considered with no false positives or false negatives. It is noteworthy that a primary caveat for optimal performance of this model is strong correlation between the SERS spectral signatures in the training basis set and the SERS viral spectral signatures considered for classification. Accordingly, high substrate spatial uniformity and substrate-to-substrate reproducibility are essential.

 is with the U.S. Army Research Laboratory, Sensors and Electron Devices Directorate, Optics & Photonics Integration Branch, Radiometric Sensor Development and Applications Team, Adelphi, Maryland.

(1) "Biological warfare," Wikipedia, The Free Encyclopedia. 8 Dec 2005; 

en.wikipedia.org/w/index.php?title=Biological_warfare&oldid=30645865

Biohazard: The Chilling True Story of the Largest Covert Biological Weapons Program in the World — Told from Inside by the Man Who Ran It

(5) The Royal Society. "Measures for controlling the threat from biological weapons". Accessed November 2006. Available from URL: 

www.royalsoc.ac.uk/displaypagedoc.asp?id=11409

 (Academic Press, San Diego and London, 1994), Chapter 3.

(10) K.L. Norrod, L.M. Sudnik, D. Rousell, and K.L. Rowlen, 

 (John Wiley and Sons, New York, 1989).  

Chemometric Techniques for Quantitative Analysis

Surface-enhanced Raman spectroscopy (SERS) has experienced an explosive resurgence in interest lately. Development of reproducible, spatially uniform SERS-active substrates has made this technique an attractive approach for identification of Raman-active compounds and biological materials including toxins, intact viruses, and intact bacterial cells–spores...

Surface-Enhanced Raman Spectroscopy: A New Approach to Rapid Identification of Intact Viruses

The topic of this column is the role and content of a validation master plan (VMP). I want to discuss where it can be used within the analytical laboratory to summarize your approach for validation of computerized systems and qualification of analytical instruments, as well as to provide a list of systems and their current status. We will look at the definition of a VMP, what the regulations say, how to interpret them for a laboratory environment, and finally, look at how we can manage our spectrometry systems and their validation status effectively within this document. Although I will not be discussing this in great detail, the validation master plan must consider instrument qualification as well as the computerized system validation aspects of laboratory equipment as part of an integrated approach to automated instrument qualification and computerized system validation. 

Before I dive in and start discussing a VMP in depth, I'd like to define the terminology used in this column to ensure that we all understand what I am talking about (I did try to avoid using the phrase on the same page but the temptation was too great so it is sneaked in). Plans are very important in computer validation, as they define what you are going to do and what you will have as evidence of actions. However, there is a terminology issue, as we can have a number of plans:  

What do these plans really do? The term master validation plan is, in my view, another name for a validation plan and will not be used any further in the column. To use it further only complicates the issue when we should look for simplification. Therefore, for the purposes of this discussion I will focus only on two: validation plan and validation master plan. 

Validation plan: This is the controlling document for the validation of a single computerized system. It defines who is involved and what they do (roles and responsibilities) as well as the life cycle phases to be followed and the anticipated documented evidence to be written to support the validation. It is a high-level document that does not detail what testing is to be undertaken, as that is the role of a test plan. 

The FDA in Section 4.5 of the General Principles of Software Validation notes, "The software validation process is defined and controlled through the use of a plan. The software validation plan defines "what" is to be accomplished through the software validation effort. Software validation plans are a significant quality system tool. Software validation plans specify areas such as scope, approach, resources, schedules, and the types and extent of activities, tasks, and work items" (1).  

VMP: Rather than present my own views, I leave it to the regulators: A VMP is a document that summarizes the firm's overall philosophy, intentions, and approach to be used for establishing performance adequacy (2). In essence, the document can be very wide ranging and summarizes the approach to validation for a site, division, department, and computerized and laboratory systems. As such, the document must have a scope statement to set the boundaries and define what is in scope and what is outside of it. During this column, I'll focus on the analytical-spectroscopy laboratory. 

In the United States, there is no specific requirement for a VMP; however, many inspectors expect an inventory of computerized systems. In contrast, under European Union (EU) GMP Annex 15, there is a regulatory requirement and a regulatory expectation for a VMP (3). The main requirements for computerized systems are defined in EU GMP Annex 11 (4), but because the regulations were written in 1992, there is currently no linkage with Annex 15. However, as Annex 11 is currently under revision, I expect to see the updated Annex 11 include the requirement for a VMP for computerized systems. 

EU Annex 15 covers qualification and validation but was derived from an earlier Pharmaceutical Inspection Convention/Pharmaceutical Inspection Cooperation Scheme (PIC/S) document, as we shall see. PIC/S is an organization for the major regulatory agencies in Europe as well as Japan, Canada, and Australia. Why this organization is important in the U.S. is that the FDA is now applying for membership in the organization, whereas previously it had observer status. There are a number of publications produced by PIC/S that are available for download from their web site at 

. One of these has the snappy title of "Recommendations on Validation Master Plan, Installation and Operational Qualification, Non-Sterile Process Validation and Cleaning Validation" (2). This document was issued originally in 1996 but was updated in 2001. It is this document that was used as the basis for Annex 15. 

However, in writing Annex 15, the authors butchered the content of the VMP section of the PIC/S document down to a series of bullet points covering half a page rather than the four pages in the source document — but the heart of the document was lost in translation (from English to English?). Therefore, to understand fully the role and content of what a VMP really is and the thought behind its content you should read the original PIC/S document (2). 

The biggest problem when joining a new company or auditing or inspecting one is finding how things get done and what's going on. Computerized system validation and instrument qualification are both multidisciplinary jobs involving various professions and skill sets — a VMP is a way to organize and manage the multiple jobs over several laboratories or departments, or within a facility. Think of the poor soul in QA wondering what the status is of various validation projects going on in the laboratory — here's the solution! The VMP can present the approach, the systems, their current validation status, and the timescales for any planned or ongoing work in a nutshell.  

To quote the PIC/S guide (2): The VMP should present an overview of the entire validation operation, its organizational structure, its content and planning. The core of the VMP being the list-inventory of the items to be validated and the planning schedule. In writing a VMP you will help management, quality assurance, laboratory staff, any validation personnel, and inspectors. What you need to do is define the scope of the document: What is in and what is out? 

This is where you need to be smart but also careful when interpreting the PIC/S document for computerized system validation. The guidance mentions manufacturing and process equipment, so you'll need to define the scope with the slant to the laboratory. Although with such a flexible document, there is nothing to stop you having a laboratory section within it or indeed a VMP for the laboratory. 

So the scope of the document will be defined by a number of factors:  

Physical boundaries: site, division, building, department, or laboratories. Indeed, a VMP for some activities such as Part 11 can go across sites.

Driver for the plan: This can be a manufacturing process, site computerized systems, or laboratory systems (not forgetting your army of spreadsheets!).

IT infrastructure: This can be included or excluded depending upon the size of the operation. Typically, this is excluded in larger organizations, as it is the responsibility of corporate IT; however, in smaller organizations it can be included within the scope of a VMP. My inclination is to exclude this and have IT take care of the work; however, this must be documented in the exclusions section of the document.

Defining the qualification, validation, and calibration activities that will contribute to the overall control over these systems.

All activities covering prospective and retrospective validation and qualification of systems within scope as well as any revalidation and requalification tasks.

Just as important as defining what is under the scope of the VMP is stating what is excluded from the scope of the document. Therefore, there must be a section stating what is excluded from the plan together with a rationale: Systems with no regulatory impact (excluded from validation but listed in the appropriate section of the inventory) and IT infrastructure (if under separate responsibility of the IT group). 

A VMP is also a formal document that should be reviewed and released by management and QA. Who signs the document depends upon the scope of the VMP; it could be the site head or a department head. What should be clear is the role of each signatory to the document: Technical author, technical review, compliance review, and approval followed by the document release. 

Inspectors and auditors are not impressed by a document's size — or thud factor when it hits the desk. Being a suspicious bunch, we like to think there is something hidden, and that motivates a desire to find out what it is. Alternatively, if you have a canny inspector, they'll ask the questions and you'll do the hunting for them! Therefore, what you want is a summary document that should therefore be brief, concise, and clear. It should not repeat information documented elsewhere, but refer to existing documents such as policy documents, standard operating procedures, and validation protocols and reports (2). So you need a section on referenced documents and just a summary of your laboratory's approach to validation and qualification. Got the message?

Let's move from theory to practical: What should a VMP contain? Table I shows the main sections of a VMP that has been adapted from the PIC/S guidance (2) and modified in light of my experience in writing these documents for laboratories and organizations. Where possible, keep the writing short and cross-reference your policies, guidelines, and procedures. Remember the PIC/S guidance: The document should be concise. 

Table I: Sections and subheadings of a laboratory validation master plan 

If one word is needed to summarize what we are trying to achieve through a number of activities such as validation and qualification, it is "control." The question that any inspector would ask is: "Are you in control?" Control, in this context, is a concept that covers your processes, instrumentation, and analyses. The main way to achieve control is through one or more of the following:  

Calibration of instruments against standards (metrology based)

Calibration of instrument against the method (system suitability test or equivalent)

Of these, the last is only a response to a problem — it's broken, so fix it. The remainder are either planned activities or in-analysis mechanisms to demonstrate that the spectrometer system is under control. Therefore, VMP must reflect what the laboratory will do to achieve this; not in detail, but cross-referencing your key procedures covering all of these areas. We must get a balance between computer validation and the other control mechanisms available to us in the laboratory. Why produce piles of paper if you run a calibration to show the instrument and software are working over the operational range required? 

As noted earlier, the heart of the VMP is the systems inventory (2). Here we'll look at how it must be set up and maintained. From Table I, there should be a listing of high-and low-risk systems and those not requiring validation. OK, that's easy. However, we also need a little more information appended to each system: 

Who is responsible for the system (system owner)?

Focus on Quality

Columnist Bob McDowall discusses the role of a validation master plan (VMP) for summarizing a laboratory's approach to computer validation.

Validation of Spectrometry Software: Understanding the Role and Content of a Validation Master Plan

Hitachi introduces two new versions of the U-4100 UV-Vis-NIR Spectrophotometer: the Standard Solid Sample System and Large Solid Sample System. These new systems use PMT and InGaAs detectors, providing increased sensitivity in the range of 850–1700 nm. This results in reduced noise, making the instrument suitable for the measurement of low transmitting/reflecting anti-reflection coatings. 

The new ChemID software from Smiths Detection is designed for use with the IdentifyIR miniaturized FT-IR analysis system. According to the company, the software allows the user to easily search library databases for sample identification, while modules within the software allow peak picking and functional group analysis of the sample. 

Bruker Optics' new MultiRAM is a fully automated Fourier transform Raman spectrometer for research applications in academia, industry, and government labs. According to the company, the product is ideal for the routine, nondestructive analysis of both organics and inorganics, such as nanomaterials and pharmaceutical raw products and formulations. 

FOSS NIRSystems offers a new probe for in-situ monitoring of mammalian cell culture growth media, with a pathlength optimized for culture media, making the design sterilizable-in-place (SIP). According to the company, the small diameter probe design is adapted for use in benchtop bioreactors and for scale up to pilot plant and manufacturing reactors. 

The TrayCell cuvette from Hellma reportedly uses the surface tension of 4 μL of sample to form a repeatable optical path of 1 mm ± 0.02 mm. According to the company, it is compatible with most spectrophotometers, operates at temperatures ranging from 5 °C to 50 °C, and has a wavelength range of 190–800 nm. Applications include biotechnology, drug discovery, and forensics.  

The new AS 200 from Retsch is a tap sieve shaker that offers dedicated versions for 50 Hz and for 60 Hz and covers a measuring range from 20 μm to 25 mm. According to the company, the combination of horizontal circular sieving motion with vertical tapping motion is specified in various standards for particle analysis (abrasives, cement, spices, etc.). 

The new Thermo Scientific Nicolet™ iN10 MX infrared microscope reportedly provides chemical images at very fast speeds utilizing Ultra-Fast Mapping, which maps a 1 mm × 1 mm area in four seconds. According to the company, Micro-ATR Mapping measures samples as small as 3 μm. 

Inorganic Ventures introduces its 2008–2009 catalog of stocked certified reference materials, featuring standards for ICP, ICP-MS, IC, AA, wet chemistry, and the latest EPA methods. According to the company, products are color-coded by application and indexed by both catalog number and subject. 

Jordan Valley's RoHS MicroAnalyzer is designed to determine the precise level of restricted substances in components. The analyzer uses a high resolution detector, an integrated CCD camera, and an X-ray tube with variable spot size to accommodate large and small samples to measure low levels of Cd, Pb, Hg, Br, and Cr in alloys, plastics, circuit boards, resins, coatings, and packaging materials. 

The BAC151 from B&W Tek is a video microscope sampling system compatible with B&W Tek's complete line of laboratory and industrial Raman probes, featuring a color video camera for alignment and viewing of the measurement field. According to the company, the system also has two LED illuminators, coaxial or off-axis, for clear viewing of various sample materials. 

The GladiATR diamond ATR accessory from PIKE is now available with an LCD, which enables positioning the sample to desired areas for analysis of visible sample features and for easing product defect analysis. According to the company, illumination and camera image are integrated below the diamond crystal, providing the ability to visualize features from opaque as well as transparent samples. 

Thermo Fisher Scientific's iCE 3000 Series of atomic absorption spectrometers has been designed to facilitate prolonged and trouble-free operation and accommodate both flame and furnace analyses. According to the company, the instruments primarily address the analytical needs of users working in food safety as well as environmental, clinical, pharmaceutical, metals, and materials industries. 

Bruker AXS has launched the S2 PICOFOX, a new portable ultra-sensitive benchtop TXRF System to quantify elemental concentrations from 0.1 ppb to % levels. According to the company, the product was made for the analytical laboratory and for field use, thus enabling TXRF ultra-high sensitivity elemental analysis in clinical, nutritional, environmental, and mining applications. 

Alfa Aesar's 2008–2009 catalog features over 3,000 new products, many of which are novel fine organics, including hundreds of new boronic acids, silanes and silanols, chiral diamines, fluorochemicals, and more. Other new products include nanomaterials, base metal fuel cell catalysts, fuel cell components, pure metals, alloys, analytical standards, and catalyst/ligand kits. 

Departments

Hitachi introduces two new versions of the U-4100 UV-Vis-NIR Spectrophotometer: the Standard Solid Sample System and Large Solid Sample System. These new systems use PMT and InGaAs detectors, providing increased sensitivity in the range of 850–1700 nm. This results in reduced noise, making the instrument suitable for the measurement of low transmitting/reflecting anti-reflection coatings.

Products

In a quantitative analysis using values measured with mass spectrometry (MS) — usually values associated with ion intensities — a nonlinear regression (weighted or nonweighted) might model the proportionality between measured instrument response and sample amount more accurately. We begin by considering general factors that might cause deviation from linearity at the upper and lower limits of a calibration curve. Recall that in quantitative spectrophotometry (in which a linear relationship is established by the equation 

 is the measured absorbance, ε the molar extinction coefficient, 

 the sample concentration), the phenomenon of self-absorbance is presented as one of many reasons for a "deviation from Beer's law," resulting in a "turning over" of the measured absorbance curve at higher sample concentrations (Figure 1). In the simplest of terms, this phenomenon results when sample molecules no longer interact independently with the incident light beam. For some molecules in solution, the intensity of the incident light has been attenuated already by prior interaction with other sample molecules in the cell. Beer's law and its deviations is a popular undergraduate laboratory experiment, and for an intermittently tongue-in-cheek discussion of Beer's law as applied to the analysis of dark beers, see reference 1. Statistical and nonlinear methods have been developed over many years to extend the useful range of quantitation using Beer's law. 

Analogous deviations from linearity of a calibration line can occur in quantitative MS. For example, a space charge effect related to the absolute number of ions in an ion source, or stored within an ion trap, can act to decrease the intensity of the expected signal. The "self-chemical ionization" phenomenon (again in ion traps) can transfer ion intensity from the molecular ion to the protonated molecule at higher sample concentrations. The central tenet of these "deviations" is that each sample molecule no longer acts independently of the presence of all the others. The performance of ionization sources, especially as they become smaller, can become exquisitely dependent upon the absolute amount of sample present. Depending upon the ion chemistry of the sample, the ion intensities chosen for measurement, and the matrix, the desired simple linear relationship between the amount of sample and a measured ion intensity value can become complicated. For example, and especially for lower mass ions, multiple paths of formation for any one particular ion can be implicated. Additionally, at higher concentrations of sample, multiple isobaric ion species at the same nominal ion mass can appear and overlap, especially as resolution requirements are relaxed in a quest for higher signal levels. Interestingly, in the case of matrix effects, higher concentrations might result in a deviation in measured signal intensity in either direction, depending upon a myriad of unexplored details. Sometimes these multiple factors can conveniently offset each other to maintain an apparent linearity. But most often, higher sample concentrations cause some decrease in the expected measured signal intensity. Remember, too, that an ionization source, and the extraction of ions from it, represents a specific snapshot of ions formed within a certain time window. Higher sample concentrations sometimes invoke complicated kinetics and higher order reactions that depend upon sample concentrations. Fascinating chemistry can be found there. Stressed analysts and us old folk usually prefer the predictable comfort of the linear range.  

At the lower end of the calibration curve in spectrophotometry, to reuse the example, deviations from Beer's law again are due to a number of factors, including (at the lowest levels) random detector noise. However, in quantitative MS, analysts seldom make measurements at or near the instrumental noise limit. Instead, other factors conspire to establish a lower limit of detection (LOD) usually delimited by matrix effects and chemical noise. For example, one oft-encountered phenomenon of lower-limit operation is that no measured response is seen until sample concentration reaches a certain minimum value. This phenomenon usually is attributed to unspecified sample losses. In the vernacular, we refer to "feeding the system." Mass spectrometrists, of course, are required by revered spectroscopic fraternity tradition to attribute such losses as occurring solely in the chromatographic column that precedes the mass spectrometer. A gas chromatography (GC) column, for example, along with its associated injection port, transfer lines, and connections, might contain a variety of active sites that can seemingly irreversibly absorb small amounts of sample. In GC, a carrier compound is sometimes used to minimize this sample loss and extend the linear proportionality of response to lower values. Of course, the sample loss problem also exists for components of the mass spectrometer. It is usually limited to the sample introduction system and ionization source components of the mass spectrometer, since system operation past that point usually involves ions held in vacuum, far removed from active sites in the physical environment. Note that if the effect of active sites, wherever they might be found, is significant, the order in which standards are run might create an apparently variable instrument response. As an example, results at the end of the day might seem to reflect a higher "sensitivity" than those at the beginning of a series of runs. Unexplained but repetitive trends in analytical results can be the result of such unappreciated phenomena. 

The more complicated the overall analytical schema, including multiple stages of MS as in MS-MS, the more likely that the individual contributions to variance are not assignable. However, even if the source of variance is unknown, if it is predictable, then the contribution can be modeled and the quantitative analysis can proceed nevertheless. A mathematical model that best fits observed data can be described mathematically as exponential, logarithmic, Gaussian, or others, or combinations thereof. An analyst might seek a subsequent linearization of the data (as in the Beer's law relationship described earlier), but should recognize that this process itself affects any subsequent treatment of errors. A direct nonlinear (and sometimes weighted) model can be used to "fit" the data directly, and the requisite mathematics is often within the scope of the commercial spreadsheet or simple data analysis programs on a personal computer. Remember that the mathematical model is only that — it is not an explanation of underlying molecular reactions and ionic dynamics, even if the fit to the data is excellent. Nonlinear regression is described in several classic textbooks (2,3) and is a component of several free and commercial statistics software packages (4). The current column is a simplified exposition of a complex topic covered in more detail in those resources.  

Consider first the shape of the calibration curve illustrated in Figure 1, in which the measured instrument response "turns over" at higher sample concentrations. As noted, a curve of this shape can be found in quantitative spectrophotometry, but it also can be seen in MS when, for example, the intensity of a measured ion (or ions) is reduced by a reaction that exhibits a power dependence on sample concentration. As the curve bends over, the projection of error from the 

 axis grows, as shown in the figure. In other words, in determining the sample concentration from the calibration, the range of possible answers (with the same analytical uncertainty) grows. This seems especially counterintuitive, as it would seem that a higher measured signal level should lead to greater accuracy. Now consider the alternative response curve shown in Figure 2, with the observed instrumental response at higher concentrations arcing in the other direction. While such a situation is encountered more rarely, it can be encountered in an MS quantitation when the intensity of a molecular ion is monitored, and a higher sample concentration leads to a situation in which internal energy in that ion is relaxed (as in collisional quenching). At first, we might think this might be desirable in that a magnified proportionality between response and sample amount would lead to a better ability to determine the sample concentration. The expected random errors in response measurement project onto a smaller breadth of possible answers on the 

 axis. What might occur instead, however, is that the lack of true independence in the creation of standards becomes evident. That is, an error associated with the creation of standards of "known" concentration amplifies the variations seen in the measured instrument responses. Perhaps most undesirable, at least on first examination, is a situation in which a complex curve is seen. Even this situation can be modeled accurately with statistics, and errors on both axes examined.  

What is a nonlinear regression? Simply put, the parameter or parameters that determine the measured response do not occur as linear combinations of independent variables. Examples include the exponential or logarithmic functions that link instrument response to amount of sample. But S-shaped curves, and Gaussian and Lorentzian functions, are also nonlinear fits to data. If the model is physically realistic, the match between experimentally measured data and model can be extraordinary. Note that this quantified curve fit is different from a similarly shaped curve (such as Figure 1) in which deviations from the assumed linear model are apparent. A nonlinear regression can be appropriate when the number of significant independent variables changes as the concentration range is traversed. Again, limitation of the analysis to a more constricted linear range dependent on only one variable simplifies the best-fit mathematics, but the broader analytical range can still be modeled adequately with nonlinear regression methods. 

Most nonlinear regressions are accomplished mathematically without a weighting scheme. As in the linear least-squares regression, the mathematical program minimizes the sum of the squared vertical distances of the model curve from the actually measured values. The assumption in this treatment is that factors that lead to scatter in the experimentally measured values are equal at all points along the curve. This assumption might be realistic over a single decade of sample concentrations, but it becomes suspect as the breadth of the calibration curve extends to two or three orders of magnitude. As a matter of experience, the observed variation in repetitive measurements of an experimental intensity for a single independent variable value often is related to the absolute values themselves. Simply stated, the observed range of errors is proportional to the value, as illustrated conceptually in Figure 3. Statistical tests can be used to establish the extent of this variance. 

What is the practical consequence of this dependence? One consequence is that the regression analysis is best accomplished with a weighting method. The 1/

 weighting minimizes the sum of the squared values for relative distances of experimental points from the best-fit curve. This weighting method is appropriate when, as shown in Figure 3, the expected errors are higher at higher measured values. In this instance, although the absolute size of the error bar at higher 

 values increases, the relative value ([range of errors in 

) is assumed to remain constant. Again, this is a mathematical assumption, and not necessarily the real observed or real physical model. In situations in which another function describes the distribution of error bar widths, a different weighting scheme might be appropriate (the 1/

 weighting is the most commonly found in nonlinear regression used in quantitative MS.  

For example, Uran and colleagues (5) recently described a liquid chromatography–electrospray ionization MS (LC–ESI-MS) method for the quantitation of 

 C-lactate in dog blood after derivatization of these analytes with 3-nitrophenyl hydrazine. A response range across three orders of magnitude was determined using internal standards and a nonlinear equation of the form 

. This example illustrates the use of a nonlinear regression analysis in a classic quantitative MS analysis based upon the proportionality between measured instrument response and the amount of the targeted compound. Moreover, the method is applied to a situation in which MS is uniquely capable — the analysis of a nonradioactive isotopically labeled sample. MS is replete with examples in which nonlinear regression methods are used at some other point in the measurement, perhaps several steps removed from a quantitation goal. For example, Daikoku and colleagues (6) successfully used energy-resolved MS in an ion trap to differentiate between 16 structural isomers of disaccharides. Sigmoidal plots for the decay of the sodiated parent ion intensity and the growth of a characteristic product ion intensity were recorded as a function of ion collision energy, with the collision energy established by variation of the rf amplitude applied to the end-cap electrode of an ion-trap mass spectrometer. Data collected in a number of data sets (between 13 and 21, depending upon the isomers) were treated using a nonlinear regression analysis to accurately model the growth and decay curves. Once this was completed, parameters used to describe the curves were displayed in a scatter plot. A visual comparison of the growth and decay curves served to differentiate many of the isomers. However, use of a nonlinear regression and a statistical evaluation of the scatter plot (which takes into consideration the confidence levels for each set of parameters) allowed all 16 isomers to be differentiated clearly. This sophisticated data treatment is predicated upon the fact that the parent ions are created in a standard manner that can be duplicated. Thus, the challenge underlying such a sophisticated analysis extends to our ability to control the ionization process through which ions are formed and sampled, as well as our ability to maintain our instruments in a stable and well-defined state. 

We note that recent texts on quantitative MS are available to the analyst (7–9). These texts boldly advance into the full world of quantitative MS and include details on such topics as homoscedastic and heteroscedastic models, and the proper statistical models for each of these two different types of variance. These models form the basis of the weighting schemes discussed earlier. We have studiously limited ourselves to the nonmathematical models in this column. Additionally, the role of quantitative MS in proteomics recently has been reviewed separately (10). Each of the methods used in quantitative MS must derive from a sound and thorough understanding of statistics, and a consensus set of definitions for the community. In this regard, the overview article of Mark (11) on testing the linearity of an analytical method is highly recommended. 

In the final installment of this series, we will describe a few examples of quantitative MS well removed from the classic linear calibration to determine the amount of sample. Such examples are drawn from topics as diverse as ion kinetics and heuristics. Additionally, a few graphical examples of quantitation will be drawn from the recent published literature. These examples will be anonymous, and will be chosen because they can leave the reader a bit skeptical, or wishing for additional data. Egregious errors in quantitative MS seldom survive peer review of results for validity or publication. However, at the same time, reviewers seldom have time to explore voluminous data in depth to confirm the validity of the statistical methods applied. Examples that leave us a bit unsure or unconvinced remind us all to make the full appropriate effort in both data collection and presentation. 

asserts that quantitative MS is one of the top 10 (±1) achievements of modern analytical science. Unrelenting pressure to provide answers more quickly at lower limits of detection for samples drawn from more complex matrices distracts attention from new ion chemistries and new informatics that will develop over the next decades. Views in this column are those of the author and not the National Science Foundation. KLB can be reached at 

brewingtechniques.com/brewingtechniques/beerslaw/index.html

(3) R.M. Bethea, B.S. Duran, and T.L. Bouillion, 

Statistical Methods for Engineers and Scientists

(4) "Fitting Models to Biological Data Using Linear and Nonlinear Regression," April 2003, available at 

(5) S. Uran, K.E. Landmark, G. Hjellum, and T. Skotland, 

(6) S. Daikoku, T. Ako, R. Kato, I. Ohtsuka, and O. Kanie, 

(7) M.W. Duncan, P.J. Gale, and A.L. Yergey, 

The Principles of Quantitative Mass Spectrometry

 (Rockpool Productions, Denver, Colorado, 2006). 

(8) I. Lavagnini, F. Magno, R. Seraglia, and P. Traldi, 

Quantitative Applications of Mass Spectrometry

Trace Quantitative Analysis by Mass Spectrometry

(10) M. Bantscheff, M. Schirle, G. Sweetman, J. Rick, and B. Kuster, 

Mass Spectrometry Forum

In the fourth part of a five-part series, columnist Ken Busch discusses factors that might cause deviation from linearity at the upper and lower limits of a calibration curve.

Quantitative Mass Spectrometry Part IV: Deviations from Linearity

X-ray fluorescence spectroscopy (XRF) is one of the primary analytical tools used in the cement industry for a variety of related applications. The principle of XRF is relatively simple; a source directs X-rays onto the atoms of the sample, ejecting electrons from the inner electron shells. As electrons fall into the resulting holes, characteristic X-rays are released, which can be analyzed by a spectrometer to identify the elementary composition of the sample. The two main types of XRF instruments, energy dispersive (EDXRF) and wavelength dispersive (WDXRF), use different methods to analyze the X-ray spectrum. Generally speaking, WDXRF offers finer spectral resolution, but has a higher price tag.  

Despite the greater expense, WDXRF is indispensible in the cement industry.  Samples include raw materials like sand and lime, finished cement products, and samples grabbed from the production process. The elemental composition of samples gives information about the purity of the sample, but also the state of oxidation of the cement, an important measure of quality. EDXRF is also used, typically in a supporting role, for the same types of applications. Samples are prepared either as pressed pellets or, for greater homogeneity, fused into a glass disk.

As an industry, cement is currently of enormous importance in Asia, which is home to more than two-thirds of both production and consumption of cement.  China alone is responsible for about half the global market. This has some environmental implications as well, as China is now the largest global source of CO

 emissions, with cement production responsible for nearly 10% of the country's CO

Some of the foregoing data were extracted from SDi's market analysis and perspectives report entitled 

Global Assessment Report, 9th Edition: The Laboratory Life Science and Analytical Instrument Industry, September 2006

. For more information, contact Michael Tice, VP of Consulting Services, Strategic Directions International, Inc., 6242 West-chester Parkway, Suite 100, Los Angeles, CA 90045, (310) 641-4982, fax: (310) 641-8851, 

Market Profile Column

X-ray fluorescence spectroscopy (XRF) is one of the primary analytical tools used in the cement industry for a variety of related applications. The principle of XRF is relatively simple; a source directs X-rays onto the atoms of the sample, ejecting electrons from the inner electron shells.

Market Profile: Cement Analysis by XRF

The 57th Annual Denver X-Ray Conference on Applications of X-Ray Analysis (DXC), sponsored by the International Center for Diffraction Data (ICDD), will be held from August 4–8 at the Denver Marriott Tech Center Hotel in Denver, Colorado. 

The Denver X-Ray Conference is the world's leading forum for scientists in the field of X-ray materials analysis. The conference features workshops, posters, and a variety of sessions on training, education, and applications for both newcomers in the field and those with many years of experience. In addition to the scientific sessions, leading manufacturers of X-ray equipment will be showcasing their latest devices and answering technical questions in the exhibit hall. DXC 2008 is a joint meeting with The 8th International Conference on Residual Stresses, sponsored by ICRS-8.

Workshops begin Monday morning, August 4, and continue through Tuesday afternoon, August 5, on the topics of XRD, XRF, and XRD & XRF combined. These workshops include "Texture Analysis with Area Detectors," "Introduction to Rietveld," "Quantitative Analysis," "Basic XRF," "Non-ambient XRD," "Combined Use of X-rays and Neutrons," "Energy Dispersive XRF," "ICRS-8 Workshop on Stress Analysis," "Cultural Heritage and Conservation," "High-Throughput X-rays," "XRF Specimen Preparation," and "Trace Analysis." 

2008 Birks Award presented to Rene Van Grieken University of Antwerp, Antwerp, Belgium

Presented by Tim Elam, EDAX/University of Washington, Redmond, Washington

2008 Jerome B. Cohen Student Award (winner to be announced at the plenary session)

Presented by Cev Noyan, Columbia University, New York, New York

2008 McMurdie Award presented to Jeffrey Dann Sylvania, Towanda, Pennsylvania

Presented by Thomas Blanton, Eastman Kodak Company Research Labs, Rochester, New York

Poster sessions will take place on August 4 and August 5 from 6 pm to 8 pm. There will be a plenary session titled "Stress and Society" given at 8:30 am on August 6.

For more information, visit the DXC web site at 

. The information contained in the program is current as of the printing date.

A preview of the 57th Annual Denver X-Ray Conference, to be held August 4–8 in Denver, Colorado.